{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Dataflowr](https://raw.githubusercontent.com/dataflowr/website/master/_assets/dataflowr_logo.png)](https://dataflowr.github.io/website/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cKEoXRSzUUCv"
   },
   "source": [
    "# Collaborative filtering: refactoring the code\n",
    "-----\n",
    "\n",
    "In this practical, you will need to refactor the code seen during the lesson in order to deal with the [Movielens 1M Dataset](https://grouplens.org/datasets/movielens/1m/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OeHGO4qbUUC4"
   },
   "source": [
    "## 1. Preparations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "frj5rX9wUUC_"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_42971/3635309985.py:5: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  import imp\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os.path as op\n",
    "import imp\n",
    "import numpy as np\n",
    "\n",
    "from zipfile import ZipFile\n",
    "try:\n",
    "    from urllib.request import urlretrieve\n",
    "except ImportError:  # Python 2 compat\n",
    "    from urllib import urlretrieve\n",
    "\n",
    "# this line need to be changed:\n",
    "data_folder = '/content/'\n",
    "\n",
    "\n",
    "ML_1M_URL = \"http://files.grouplens.org/datasets/movielens/ml-1m.zip\"\n",
    "ML_1M_FILENAME = op.join(data_folder,ML_1M_URL.rsplit('/', 1)[1])\n",
    "ML_1M_FOLDER = op.join(data_folder,'ml-1m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "utzHaMDsV1Bq"
   },
   "outputs": [],
   "source": [
    "if not op.exists(ML_1M_FILENAME):\n",
    "    print('Downloading %s to %s...' % (ML_1M_URL, ML_1M_FILENAME))\n",
    "    urlretrieve(ML_1M_URL, ML_1M_FILENAME)\n",
    "\n",
    "if not op.exists(ML_1M_FOLDER):\n",
    "    print('Extracting %s to %s...' % (ML_1M_FILENAME, ML_1M_FOLDER))\n",
    "    ZipFile(ML_1M_FILENAME).extractall(data_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hHgLg4jBUUDp"
   },
   "source": [
    "## 2. Data analysis and formating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZlGyPSBpUUDt"
   },
   "source": [
    "As in the lesson, we start by loading the data with [Python Data Analysis Library](http://pandas.pydata.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qk6Vp83IV1Bv"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  ratings  timestamp\n",
       "0        1     1193        5  978300760\n",
       "1        1      661        3  978302109\n",
       "2        1      914        3  978301968\n",
       "3        1     3408        4  978300275\n",
       "4        1     2355        5  978824291"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "all_ratings = pd.read_csv(op.join(ML_1M_FOLDER, 'ratings.dat'), sep='::',\n",
    "                          names=[\"user_id\", \"item_id\", \"ratings\", \"timestamp\"],engine='python')\n",
    "all_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UDPeRguMV1Bz",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  item_id                           item_name\n",
       "0       1                    Toy Story (1995)\n",
       "1       2                      Jumanji (1995)\n",
       "2       3             Grumpier Old Men (1995)\n",
       "3       4            Waiting to Exhale (1995)\n",
       "4       5  Father of the Bride Part II (1995)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_movies_names = []\n",
    "list_item_ids = []\n",
    "with open(op.join(ML_1M_FOLDER, 'movies.dat'), encoding = \"ISO-8859-1\") as fp:\n",
    "    for line in fp:\n",
    "        list_item_ids.append(line.split('::')[0])\n",
    "        list_movies_names.append(line.split('::')[1])\n",
    "        \n",
    "movies_names = pd.DataFrame(list(zip(list_item_ids, list_movies_names)), \n",
    "               columns =['item_id', 'item_name']) \n",
    "movies_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6nVdjC0IV1B3"
   },
   "source": [
    "Here we add the title of the movies to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "21gIXEkXV1B4"
   },
   "outputs": [],
   "source": [
    "movies_names['item_id']=movies_names['item_id'].astype(int)\n",
    "all_ratings['item_id']=all_ratings['item_id'].astype(int)\n",
    "all_ratings = all_ratings.merge(movies_names,on='item_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_XJlXE1mV1B8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>item_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978298413</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978220179</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978199279</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978158471</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  ratings  timestamp  \\\n",
       "0        1     1193        5  978300760   \n",
       "1        2     1193        5  978298413   \n",
       "2       12     1193        4  978220179   \n",
       "3       15     1193        4  978199279   \n",
       "4       17     1193        5  978158471   \n",
       "\n",
       "                                item_name  \n",
       "0  One Flew Over the Cuckoo's Nest (1975)  \n",
       "1  One Flew Over the Cuckoo's Nest (1975)  \n",
       "2  One Flew Over the Cuckoo's Nest (1975)  \n",
       "3  One Flew Over the Cuckoo's Nest (1975)  \n",
       "4  One Flew Over the Cuckoo's Nest (1975)  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "enyJuYhUUUEL"
   },
   "source": [
    "The dataframe `all_ratings` contains all the raw data for our problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3M7-jinQUUEO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000209"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of entries\n",
    "len(all_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ofVcE781UUEa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.000209e+06\n",
       "mean     3.581564e+00\n",
       "std      1.117102e+00\n",
       "min      1.000000e+00\n",
       "25%      3.000000e+00\n",
       "50%      4.000000e+00\n",
       "75%      4.000000e+00\n",
       "max      5.000000e+00\n",
       "Name: ratings, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings['ratings'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tCdV2qzMUUEk"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 4, 3, 2, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings['ratings'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zU9vARwJUUEs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.000209e+06\n",
       "mean     3.024512e+03\n",
       "std      1.728413e+03\n",
       "min      1.000000e+00\n",
       "25%      1.506000e+03\n",
       "50%      3.070000e+03\n",
       "75%      4.476000e+03\n",
       "max      6.040000e+03\n",
       "Name: user_id, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings['user_id'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P1QoS71CUUE7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6040\n"
     ]
    }
   ],
   "source": [
    "# number of unique users\n",
    "total_user_id = len(all_ratings['user_id'].unique())\n",
    "print(total_user_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-Az4KDFgV1CQ"
   },
   "source": [
    "We see that as in the lesson, the users seem to be indexed from 1 to 6040. Let's check that below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Pj8ZAzmIV1CQ"
   },
   "outputs": [],
   "source": [
    "list_user_id = list(all_ratings['user_id'].unique())\n",
    "list_user_id.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l9omI-cwV1CT"
   },
   "outputs": [],
   "source": [
    "for i,j in enumerate(list_user_id):\n",
    "    if j != i+1:\n",
    "        print(i,j) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h-LytszyV1CV"
   },
   "source": [
    "We create a new column `user_num` to get an index from 0 to 6039 for users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jE6WVgT-V1CV"
   },
   "outputs": [],
   "source": [
    "all_ratings['user_num'] = all_ratings['user_id'].apply(lambda x :x-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6HldBddZV1CY"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>item_name</th>\n",
       "      <th>user_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978298413</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978220179</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978199279</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978158471</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  ratings  timestamp  \\\n",
       "0        1     1193        5  978300760   \n",
       "1        2     1193        5  978298413   \n",
       "2       12     1193        4  978220179   \n",
       "3       15     1193        4  978199279   \n",
       "4       17     1193        5  978158471   \n",
       "\n",
       "                                item_name  user_num  \n",
       "0  One Flew Over the Cuckoo's Nest (1975)         0  \n",
       "1  One Flew Over the Cuckoo's Nest (1975)         1  \n",
       "2  One Flew Over the Cuckoo's Nest (1975)        11  \n",
       "3  One Flew Over the Cuckoo's Nest (1975)        14  \n",
       "4  One Flew Over the Cuckoo's Nest (1975)        16  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W0OThRYhV1Cb"
   },
   "source": [
    "We now look at movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l3gfokbNUUFH"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.000209e+06\n",
       "mean     1.865540e+03\n",
       "std      1.096041e+03\n",
       "min      1.000000e+00\n",
       "25%      1.030000e+03\n",
       "50%      1.835000e+03\n",
       "75%      2.770000e+03\n",
       "max      3.952000e+03\n",
       "Name: item_id, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings['item_id'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8ncInsk_V1Cg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3706\n"
     ]
    }
   ],
   "source": [
    "# number of unique rated items\n",
    "total_item_id = len(all_ratings['item_id'].unique())\n",
    "print(total_item_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3gGu2LjiV1Ci"
   },
   "source": [
    "Here there is a clear problem: there are 3706 different movies but the range of `item_id` starts at 1 and ends at 3952. So there are gaps, so the first thing you will need to do is to create a new column `item_num` so that all movies are indexed from 0 to 3705."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9Zs7e-pyV1Cj"
   },
   "outputs": [],
   "source": [
    "itemnum_2_itemid = list(all_ratings['item_id'].unique())\n",
    "itemnum_2_itemid.sort()\n",
    "itemid_2_itemnum = {c:i for i,c in enumerate(itemnum_2_itemid)}\n",
    "all_ratings['item_num'] = all_ratings['item_id'].apply(lambda x: itemid_2_itemnum[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "weNJOlzcV1Cl"
   },
   "source": [
    "This function will verify that your result is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ktctMT12V1Cl"
   },
   "outputs": [],
   "source": [
    "def check_ratings_num(df):\n",
    "    item_num = set(df['item_num'])\n",
    "    if item_num == set(range(len(item_num))):\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w4w1XQnfV1Cp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_ratings_num(all_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HYa43L6EV1Cr"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>item_name</th>\n",
       "      <th>user_num</th>\n",
       "      <th>item_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>0</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978298413</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>1</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978220179</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>11</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978199279</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>14</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978158471</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>16</td>\n",
       "      <td>1104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  ratings  timestamp  \\\n",
       "0        1     1193        5  978300760   \n",
       "1        2     1193        5  978298413   \n",
       "2       12     1193        4  978220179   \n",
       "3       15     1193        4  978199279   \n",
       "4       17     1193        5  978158471   \n",
       "\n",
       "                                item_name  user_num  item_num  \n",
       "0  One Flew Over the Cuckoo's Nest (1975)         0      1104  \n",
       "1  One Flew Over the Cuckoo's Nest (1975)         1      1104  \n",
       "2  One Flew Over the Cuckoo's Nest (1975)        11      1104  \n",
       "3  One Flew Over the Cuckoo's Nest (1975)        14      1104  \n",
       "4  One Flew Over the Cuckoo's Nest (1975)        16      1104  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ur4bjuniUUFj"
   },
   "source": [
    "Now we will split the data in _train_, _val_ and _test_ be using a pre-defined function from [scikit-learn](http://scikit-learn.org/stable/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZT5oxhoGUUFm"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ratings_trainval, ratings_test = train_test_split(all_ratings, test_size=0.1, random_state=42)\n",
    "\n",
    "ratings_train, ratings_val = train_test_split(ratings_trainval, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q7JF-d05UUGc"
   },
   "source": [
    "## 3. The model\n",
    "\n",
    "We will now modify a bit the `FactorizationModel` class seen during the lesson. Internally, we will still use the `Model_dot` but now we use the PyTorch dataloader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tsrfFi1QUUGd"
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4dYlOkCzV1C0"
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P3OnBFlOV1C2"
   },
   "outputs": [],
   "source": [
    "def df_2_tensor(df, device):\n",
    "    # return a triplet user_num, item_num, rating from the dataframe\n",
    "    user_num = np.asarray(df['user_num'])\n",
    "    item_num = np.asarray(df['item_num'])\n",
    "    rating = np.asarray(df['ratings'])\n",
    "    return torch.from_numpy(user_num).to(device), torch.from_numpy(item_num).to(device), torch.from_numpy(rating).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "W2UUX0GDV1C4"
   },
   "source": [
    "Below, we construct 3 tensors containing the `user_num`, `item_num` and `rating` for the training set. All tensors have the same shape so that `train_user_num[i]` watched `train_item_num[i]` and gave a rating of `train_rating[i]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LY7lG3M_V1C5"
   },
   "outputs": [],
   "source": [
    "train_user_num, train_item_num, train_rating = df_2_tensor(ratings_train,device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SY39426KV1C7"
   },
   "source": [
    "We now do the same thing for the validation and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o1bjHQExV1C8"
   },
   "outputs": [],
   "source": [
    "val_user_num, val_item_num, val_rating = df_2_tensor(ratings_val,device)\n",
    "test_user_num, test_item_num, test_rating = df_2_tensor(ratings_test,device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HpkKu7PVUUGp"
   },
   "source": [
    "The code below is taken from the lesson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eB6_y1nMUUGq"
   },
   "outputs": [],
   "source": [
    "class ScaledEmbedding(nn.Embedding):\n",
    "    \"\"\"\n",
    "    Embedding layer that initialises its values\n",
    "    to using a normal variable scaled by the inverse\n",
    "    of the embedding dimension.\n",
    "    \"\"\"\n",
    "    def reset_parameters(self):\n",
    "        \"\"\"\n",
    "        Initialize parameters.\n",
    "        \"\"\"\n",
    "\n",
    "        self.weight.data.normal_(0, 1.0 / self.embedding_dim)\n",
    "        if self.padding_idx is not None:\n",
    "            self.weight.data[self.padding_idx].fill_(0)\n",
    "\n",
    "\n",
    "class ZeroEmbedding(nn.Embedding):\n",
    "    \"\"\"\n",
    "    Used for biases.\n",
    "    \"\"\"\n",
    "\n",
    "    def reset_parameters(self):\n",
    "        \"\"\"\n",
    "        Initialize parameters.\n",
    "        \"\"\"\n",
    "\n",
    "        self.weight.data.zero_()\n",
    "        if self.padding_idx is not None:\n",
    "            self.weight.data[self.padding_idx].fill_(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ktXRW3-4UUGt"
   },
   "outputs": [],
   "source": [
    "class DotModel(nn.Module):\n",
    "    \n",
    "    def __init__(self,\n",
    "                 num_users,\n",
    "                 num_items,\n",
    "                 embedding_dim=32):\n",
    "        \n",
    "        super(DotModel, self).__init__()\n",
    "        \n",
    "        self.embedding_dim = embedding_dim\n",
    "        \n",
    "        self.user_embeddings = ScaledEmbedding(num_users, embedding_dim)\n",
    "        self.item_embeddings = ScaledEmbedding(num_items, embedding_dim)\n",
    "        self.user_biases = ZeroEmbedding(num_users, 1)\n",
    "        self.item_biases = ZeroEmbedding(num_items, 1)\n",
    "                \n",
    "        \n",
    "    def forward(self, user_ids, item_ids):\n",
    "        #\n",
    "        # your code\n",
    "        #\n",
    "        user_embedding = self.user_embeddings(user_ids)\n",
    "        item_embedding = self.item_embeddings(item_ids)\n",
    "\n",
    "        user_embedding = user_embedding\n",
    "        item_embedding = item_embedding\n",
    "\n",
    "        user_bias = self.user_biases(user_ids).squeeze()\n",
    "        item_bias = self.item_biases(item_ids).squeeze()\n",
    "\n",
    "        dot = (user_embedding * item_embedding).sum(1)\n",
    "        \n",
    "        res = dot + user_bias + item_bias\n",
    "\n",
    "        return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PlbbwU1ze5d7"
   },
   "outputs": [],
   "source": [
    "net = DotModel(total_user_id,total_item_id).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ysX9Q9pxiMG4"
   },
   "source": [
    "Now test your network on a small batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I20UhBLZjBEs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.0078,  0.0003, -0.0097,  0.0069, -0.0105], device='cuda:0',\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = net(train_user_num[:5], train_item_num[:5])\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7sqlTLCyiwzl"
   },
   "outputs": [],
   "source": [
    "def regression_loss(predicted_ratings, observed_ratings):\n",
    "    return ((observed_ratings - predicted_ratings) ** 2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ins38kThjNl0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(12.0166, device='cuda:0', grad_fn=<MeanBackward0>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_fn = regression_loss\n",
    "loss = loss_fn(predictions, train_rating[:5])\n",
    "loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mYpYDeK0V1DN"
   },
   "source": [
    "Now you need to construct a dataset and a dataloader. For this, you can define a first function taking as arguments the tensors defined above and returning a list; then a second function taking as argument a dataset, the batchsize and a boolean for the shuffling. We will not use anymore the functions `shuffle` and `minibatch` used in the lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c1VFUdHtV1DO"
   },
   "outputs": [],
   "source": [
    "def tensor_2_dataset(user,item,rating):\n",
    "    # your code here\n",
    "    return list(zip(user,item,rating))\n",
    "    \n",
    "def make_dataloader(dataset,bs,shuffle):\n",
    "    # your code here\n",
    "    return torch.utils.data.DataLoader(dataset,batch_size=bs,shuffle=shuffle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h-Y4ruIPV1DS"
   },
   "outputs": [],
   "source": [
    "train_dataset = tensor_2_dataset(train_user_num,train_item_num, train_rating)\n",
    "val_dataset = tensor_2_dataset(val_user_num,val_item_num,val_rating)\n",
    "test_dataset = tensor_2_dataset(test_user_num, test_item_num, test_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rXvR2apHV1DU"
   },
   "outputs": [],
   "source": [
    "train_dataloader = make_dataloader(train_dataset,1024,True)\n",
    "val_dataloader = make_dataloader(val_dataset,1024, False)\n",
    "test_dataloader = make_dataloader(test_dataset,1024,False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wz1h-gG-V1DX"
   },
   "source": [
    "Here you need to modify the code seen during the lesson:\n",
    " - remove the batch_size in the init\n",
    " - the fit function should now take as argument a dataloader for the training and a dataloader for the validation. AT the end of each epoch, you run the test method on the validation set. Then you print both the loss on the training set and on the validation set to see if you are overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2ekPPr7SUUG7"
   },
   "outputs": [],
   "source": [
    "class FactorizationModel(object):\n",
    "    \n",
    "    def __init__(self, embedding_dim=32, n_iter=10, l2=0.0,\n",
    "                 learning_rate=1e-2, device=device, net=None, num_users=None,\n",
    "                 num_items=None,random_state=None):\n",
    "        \n",
    "        self._embedding_dim = embedding_dim\n",
    "        self._n_iter = n_iter\n",
    "        self._learning_rate = learning_rate\n",
    "        self._l2 = l2\n",
    "        self._device = device\n",
    "        self._num_users = num_users\n",
    "        self._num_items = num_items\n",
    "        self._net = net\n",
    "        self._optimizer = None\n",
    "        self._loss_func = None\n",
    "        self._random_state = random_state or np.random.RandomState()\n",
    "             \n",
    "        \n",
    "    def _initialize(self):\n",
    "        if self._net is None:\n",
    "            self._net = DotModel(self._num_users, self._num_items, self._embedding_dim).to(self._device)\n",
    "        \n",
    "        self._optimizer = optim.Adam(\n",
    "                self._net.parameters(),\n",
    "                lr=self._learning_rate,\n",
    "                weight_decay=self._l2\n",
    "            )\n",
    "        \n",
    "        self._loss_func = regression_loss\n",
    "        \n",
    "    \n",
    "    @property\n",
    "    def _initialized(self):\n",
    "        return self._optimizer is not None\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return _repr_model(self)\n",
    "    \n",
    "    def fit(self, dataloader, val_dataloader, verbose=True):       \n",
    "        if not self._initialized:\n",
    "            self._initialize()\n",
    "            \n",
    "        for epoch_num in range(self._n_iter):\n",
    "            epoch_loss = 0.0\n",
    "            self._net.train(True)\n",
    "\n",
    "            #\n",
    "            # your code\n",
    "            for (minibatch_num, (batch_user, batch_item, batch_rating)) in enumerate(dataloader):\n",
    "                predictions = self._net(batch_user,batch_item)\n",
    "                self._optimizer.zero_grad()\n",
    "                loss = self._loss_func(predictions,batch_rating)\n",
    "                epoch_loss += loss.item()\n",
    "                loss.backward()\n",
    "                self._optimizer.step()\n",
    "            #\n",
    "                \n",
    "            \n",
    "            epoch_loss = epoch_loss / (minibatch_num + 1)\n",
    "            loss_test = self.test(val_dataloader)\n",
    "\n",
    "            if verbose:\n",
    "                print('Epoch {}: loss_train {}, loss_val {}'.format(epoch_num, epoch_loss,loss_test))\n",
    "        \n",
    "            if np.isnan(epoch_loss) or epoch_loss == 0.0:\n",
    "                raise ValueError('Degenerate epoch loss: {}'\n",
    "                                 .format(epoch_loss))\n",
    "    \n",
    "    \n",
    "    def test(self,dataloader, verbose = False):\n",
    "        self._net.train(False)\n",
    "        L1loss = torch.nn.L1Loss()\n",
    "        test_loss = 0.0\n",
    "        test_mae = 0.0\n",
    "        #\n",
    "        # your code here (mae = mean absolute error)\n",
    "        for (minibatch_num, (batch_user, batch_item, batch_rating)) in enumerate(dataloader):\n",
    "            predictions = self._net(batch_user,batch_item)\n",
    "            loss = self._loss_func(predictions,batch_rating)\n",
    "            test_loss += loss.item()\n",
    "            test_mae += L1loss(predictions,batch_rating.type(torch.FloatTensor).to(device))\n",
    "        #\n",
    "                \n",
    "        test_loss = test_loss / (minibatch_num + 1)\n",
    "        test_mae = test_mae / (minibatch_num+1)\n",
    "        if verbose:\n",
    "            print(f\"RMSE: {np.sqrt(test_loss)}, MAE: {test_mae}\")\n",
    "        return loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qGuzPUNrUUG_"
   },
   "outputs": [],
   "source": [
    "model = FactorizationModel(embedding_dim=50,  # latent dimensionality\n",
    "                                   n_iter=5,  # number of epochs of training\n",
    "                                   learning_rate=5e-4,\n",
    "                                   l2=1e-8,  # strength of L2 regularization\n",
    "                                   num_users=total_user_id,\n",
    "                                   num_items=total_item_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Bd2CIosUUHJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: loss_train 10.483497817407954, loss_val 3.259150505065918\n",
      "Epoch 1: loss_train 1.7397940213030034, loss_val 1.0772451162338257\n",
      "Epoch 2: loss_train 0.9825870432335921, loss_val 0.91329026222229\n",
      "Epoch 3: loss_train 0.8714386894546374, loss_val 0.873589277267456\n",
      "Epoch 4: loss_train 0.8386065927569313, loss_val 0.8576654195785522\n"
     ]
    }
   ],
   "source": [
    "model.fit(train_dataloader,val_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: loss_train 0.8233853814878849, loss_val 0.8553363680839539\n",
      "Epoch 1: loss_train 0.8122792715827624, loss_val 0.8385775685310364\n",
      "Epoch 2: loss_train 0.8015975057326182, loss_val 0.8299235701560974\n",
      "Epoch 3: loss_train 0.7913516945760659, loss_val 0.8280952572822571\n",
      "Epoch 4: loss_train 0.7815644568716636, loss_val 0.8056606650352478\n"
     ]
    }
   ],
   "source": [
    "model.fit(train_dataloader,val_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: loss_train 0.7725902248663131, loss_val 0.8080278038978577\n",
      "Epoch 1: loss_train 0.7641085237264633, loss_val 0.7990661263465881\n",
      "Epoch 2: loss_train 0.7561668316372717, loss_val 0.7914382219314575\n",
      "Epoch 3: loss_train 0.7482567571028315, loss_val 0.7849618196487427\n",
      "Epoch 4: loss_train 0.7404139599565304, loss_val 0.7870027422904968\n"
     ]
    }
   ],
   "source": [
    "model.fit(train_dataloader,val_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: loss_train 0.7319444524820404, loss_val 0.7801695466041565\n",
      "Epoch 1: loss_train 0.7233793336634684, loss_val 0.7774919867515564\n",
      "Epoch 2: loss_train 0.7141669735011428, loss_val 0.7649633884429932\n",
      "Epoch 3: loss_train 0.7041290485196643, loss_val 0.771318256855011\n",
      "Epoch 4: loss_train 0.6933240536035914, loss_val 0.7632575631141663\n"
     ]
    }
   ],
   "source": [
    "model.fit(train_dataloader,val_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HKyc7Bg5UUHR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.867548660282591, MAE: 0.681933581829071\n"
     ]
    }
   ],
   "source": [
    "_= model.test(test_dataloader,True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yXVIF75IUUHW"
   },
   "source": [
    "Play with the parameter to beat the benchmarks presented here: [Surprise](https://github.com/NicolasHug/Surprise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VK3KgG47V1Di"
   },
   "source": [
    "## 4. Best and worst movies\n",
    "\n",
    "Now you need to rank the movies according to their bias. For this, you need to recover the biases of the movies, make a list of the pairs `[name of the movie, its bias]` and then sort this list according to the biases. You can use the method sort of a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7KDDTQfxV1Di"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[\"One Flew Over the Cuckoo's Nest (1975)\", 1104],\n",
       "       [\"One Flew Over the Cuckoo's Nest (1975)\", 1104],\n",
       "       [\"One Flew Over the Cuckoo's Nest (1975)\", 1104],\n",
       "       ...,\n",
       "       ['White Boys (1999)', 2638],\n",
       "       ['One Little Indian (1973)', 3367],\n",
       "       ['Five Wives, Three Secretaries and Me (1998)', 2702]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_bias_np = model._net.item_biases.weight.data.cpu().numpy()\n",
    "item_bias_np = item_bias_np.squeeze()\n",
    "np.asarray(all_ratings[['item_name', 'item_num']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "numitem_2_name = {i:name for name,i in np.asarray(all_ratings[['item_name', 'item_num']])}\n",
    "name_2_numitem = {name:i for name,i in np.asarray(all_ratings[['item_name', 'item_num']])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_name_bias = [[name, item_bias_np[name_2_numitem[name]]] for name in list(ratings_train['item_name'].unique())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Flawless (1999)', 0.1841258],\n",
       " ['Grand Day Out, A (1992)', 0.37905848],\n",
       " ['Platoon (1986)', 0.3168986],\n",
       " ['Contender, The (2000)', 0.30273548]]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_name_bias[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_name_bias.sort(key= lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Mr. Wrong (1996)', 0.03592497],\n",
       " ['Children of the Corn IV: The Gathering (1996)', 0.044052407],\n",
       " ['Free Willy 3: The Rescue (1997)', 0.04690211],\n",
       " ['August (1996)', 0.047154162],\n",
       " ['Elstree Calling (1930)', 0.048291106],\n",
       " [\"Puppet Master III: Toulon's Revenge (1991)\", 0.053831235],\n",
       " ['Tokyo Fist (1995)', 0.05392649],\n",
       " ['Fantastic Night, The (La Nuit Fantastique) (1949)', 0.058136385],\n",
       " ['Lost in Space (1998)', 0.0590787],\n",
       " ['Problem Child 2 (1991)', 0.059472192]]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_name_bias[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['American Beauty (1999)', 0.47356018],\n",
       " ['Matrix, The (1999)', 0.47521988],\n",
       " ['Usual Suspects, The (1995)', 0.4803672],\n",
       " ['Silence of the Lambs, The (1991)', 0.49111733],\n",
       " ['Godfather, The (1972)', 0.49870548],\n",
       " ['Star Wars: Episode IV - A New Hope (1977)', 0.5180059],\n",
       " ['Raiders of the Lost Ark (1981)', 0.5185158],\n",
       " [\"Schindler's List (1993)\", 0.5215493],\n",
       " ['Sixth Sense, The (1999)', 0.52186865],\n",
       " ['Shawshank Redemption, The (1994)', 0.53263825]]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_name_bias[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Dc1ZKW9XV1Dl"
   },
   "source": [
    "## 5. PCA of movies' embeddings\n",
    "\n",
    "Now you can also plpay with the embeddings learned by your algorithm for the movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HX37hFDsV1Dl"
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from operator import itemgetter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3706, 50)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_emb_np = model._net.item_embeddings.weight.data.cpu().numpy()\n",
    "item_emb_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['American Beauty (1999)',\n",
       "       'Star Wars: Episode IV - A New Hope (1977)',\n",
       "       'Star Wars: Episode V - The Empire Strikes Back (1980)',\n",
       "       'Star Wars: Episode VI - Return of the Jedi (1983)',\n",
       "       'Jurassic Park (1993)', 'Saving Private Ryan (1998)',\n",
       "       'Terminator 2: Judgment Day (1991)', 'Matrix, The (1999)',\n",
       "       'Back to the Future (1985)', 'Silence of the Lambs, The (1991)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = all_ratings.groupby('item_name')['ratings'].count()\n",
    "most_rated_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "most_rated_movies[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_rated_movies_num = [name_2_numitem[n] for n in most_rated_movies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=4)\n",
    "latent_fac = pca.fit_transform(item_emb_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_comp = [(f, numitem_2_name[i]) for i,f in enumerate(latent_fac[:,1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.4044963, 'White Boys (1999)'),\n",
       " (1.4044892, 'Match, The (1999)'),\n",
       " (1.404487, 'Frank and Ollie (1995)'),\n",
       " (1.404487, 'Number Seventeen (1932)'),\n",
       " (1.4044863, 'Uninvited Guest, An (2000)'),\n",
       " (1.4044858, 'Fausto (1993)'),\n",
       " (1.4044858, 'Hungarian Fairy Tale, A (1987)'),\n",
       " (1.4044836, 'Silence of the Palace, The (Saimt el Qusur) (1994)'),\n",
       " (1.404482, 'Foolish (1999)'),\n",
       " (1.4044805, 'Spring Fever USA (a.k.a. Lauderdale) (1989)')]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-1.0646349, 'Patriot, The (2000)'),\n",
       " (-0.99237186, 'Independence Day (ID4) (1996)'),\n",
       " (-0.98500663, 'Forrest Gump (1994)'),\n",
       " (-0.97501045, 'Top Gun (1986)'),\n",
       " (-0.93589485, 'Green Mile, The (1999)'),\n",
       " (-0.9230021, 'Braveheart (1995)'),\n",
       " (-0.90603405, 'Armageddon (1998)'),\n",
       " (-0.8840694, 'Rock, The (1996)'),\n",
       " (-0.86896414, 'Remember the Titans (2000)'),\n",
       " (-0.82824147, 'Star Wars: Episode I - The Phantom Menace (1999)')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nums = most_rated_movies_num[:80]\n",
    "txt_movies_names = [numitem_2_name[i] for i in nums]\n",
    "X = latent_fac[nums,2]\n",
    "Y = latent_fac[nums,3]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(txt_movies_names, X, Y):\n",
    "    plt.text(x+0.01,y-0.01,i, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oWomt-l-V1Dp"
   },
   "source": [
    "[![Dataflowr](https://raw.githubusercontent.com/dataflowr/website/master/_assets/dataflowr_logo.png)](https://dataflowr.github.io/website/)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "include_colab_link": false,
   "name": "08_collaborative_filtering_1M.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "dldiy",
   "language": "python",
   "name": "dldiy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
